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ffm-baseline
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417cd1d7
Commit
417cd1d7
authored
Feb 19, 2019
by
王志伟
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数据指标波动假设检验统计
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hypothesis_test.py
eda/recommended_indexs/hypothesis_test.py
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417cd1d7
#! -*- coding: utf8 -*-
import
pandas
as
pd
from
scipy.stats
import
ttest_ind
from
scipy.stats
import
levene
import
pymysql
import
datetime
from
utils
import
con_sql
#########推荐策略前后统计指标假设检验(t检验)###############
#自动获取昨日日期
def
get_yesterday_date
():
#自动获取昨天的日期,如"2018-08-08"
"""
:rtype : str
"""
today
=
datetime
.
date
.
today
()
yesterday
=
today
-
datetime
.
timedelta
(
days
=
1
)
yesterday
=
yesterday
.
strftime
(
"
%
Y-
%
m-
%
d"
)
return
yesterday
yesterday
=
get_yesterday_date
#自动获取10日前的日期
def
get_somedate
():
#自动获取10日前的日期,如"2018-07-28"
"""
:rtype : str
"""
today
=
datetime
.
date
.
today
()
someday
=
today
-
datetime
.
timedelta
(
days
=
11
)
someday
=
someday
.
strftime
(
"
%
Y-
%
m-
%
d"
)
return
someday
ten_days
=
get_somedate
()
#获取最近10天的数据
def
DATA_recently
(
x
,
y
,
z
,
q
):
sql_cid
=
"select {0}/{1} as {2} from {3}
\
where stat_date >= ten_days group by stat_date"
.
format
(
x
,
y
,
z
,
q
)
CVR_DATA_recently
=
con_sql
(
sql_cid
)[
0
][
0
]
return
CVR_DATA_recently
#获取固定时间的10天的数据
def
DATA_fixed
(
x
,
y
,
z
,
q
):
sql_cid
=
"select {0}/{1} as {2} from {3}
\
where stat_date >= "
" and stat_date<"
" group by stat_date"
.
format
(
x
,
y
,
z
,
q
)
CVR_DATA_fixed
=
con_sql
(
sql_cid
)[
0
][
0
]
return
CVR_DATA_fixed
#新用户cvr
x_crv_new
=
DATA_recently
(
"diary_meigou_newUser"
,
"diary_clk_newUser"
,
"CVR_new"
,
"diary_meigou_crv"
)
y_crv_new
=
DATA_fixed
(
"diary_meigou_newUser"
,
"diary_clk_newUser"
,
"CVR_new"
,
"diary_meigou_crv"
)
#老用户cvr
x_crv_old
=
DATA_recently
(
"diary_meigou_oldUser"
,
"diary_clk_oldUser"
,
"CVR_old"
,
"diary_meigou_crv"
)
y_crv_old
=
DATA_fixed
(
"diary_meigou_oldUser"
,
"diary_clk_oldUser"
,
"CVR_old"
,
"diary_meigou_crv"
)
#新用户ct-cvr
x_ctcrv_new
=
DATA_recently
(
"diary_meigou_newUser"
,
"diary_exp_newUser"
,
"CT_CVR_new"
,
"diary_meigou_crv"
)
y_ctcrv_new
=
DATA_fixed
(
"diary_meigou_newUser"
,
"diary_exp_newUser"
,
"CT_CVR_new"
,
"diary_meigou_crv"
)
#老用户ct-cvr
x_ctcrv_old
=
DATA_recently
(
"diary_meigou_oldUser"
,
"diary_exp_oldUser"
,
"CT_CVR_old"
,
"diary_meigou_crv"
)
y_ctcrv_old
=
DATA_fixed
(
"diary_meigou_oldUser"
,
"diary_exp_oldUser"
,
"CT_CVR_old"
,
"diary_meigou_crv"
)
#新用户ctr(page_view)
x_ctr_new
=
DATA_recently
(
"clk_count_newUser_all"
,
"imp_count_newUser_all"
,
"ctr_new"
,
"bug_Recommendation_strategy_newUser"
)
y_ctr_new
=
DATA_fixed
(
"clk_count_newUser_all"
,
"imp_count_newUser_all"
,
"ctr_new"
,
"bug_Recommendation_strategy_newUser"
)
#老用户ctr(page_view)
x_ctr_old
=
DATA_recently
(
"clk_count_oldUser_all"
,
"imp_count_oldUser_all"
,
"ctr_old"
,
"bug_Recommendation_strategy_temp"
)
y_ctr_old
=
DATA_fixed
(
"clk_count_oldUser_all"
,
"imp_count_oldUser_all"
,
"ctr_old"
,
"bug_Recommendation_strategy_temp"
)
#新用户ctr(on_click_diary_card)
x_ctr_new_o
=
DATA_recently
(
"clk_count_newUser_all_a"
,
"imp_count_newUser_all"
,
"ctr_new"
,
"on_click_diary_card"
)
y_ctr_new_o
=
DATA_fixed
(
"clk_count_newUser_all_a"
,
"imp_count_newUser_all"
,
"ctr_new"
,
"on_click_diary_card"
)
#老用户ctr(on_click_diary_card)
x_ctr_old_o
=
DATA_recently
(
"clk_count_oldUser_all_a"
,
"imp_count_oldUser_all"
,
"ctr_old"
,
"on_click_diary_card"
)
y_ctr_old_o
=
DATA_fixed
(
"clk_count_oldUser_all_a"
,
"imp_count_oldUser_all"
,
"ctr_old"
,
"on_click_diary_card"
)
def
t_test
(
x
,
y
):
#进行t检验
#策略前的数据,赋值给x,策略后的数据赋值给y,均采用10日内数据
x
=
[
2
,
4
,
2
,
3
,
4
,
2
,
3
]
y
=
[
4
,
5
,
6
,
3
,
4
,
5
,
6
]
#检验数据方差是否齐性
a
=
levene
(
x
,
y
)
p_value
=
a
[
1
]
#结果若p_value>0.05,则认为两组数据方差是相等的,否则两组数据方差是不等的
if
p_value
>
0.05
:
#认为数据方差具有齐性,equal_var=ture
t_test
=
ttest_ind
(
x
,
y
,
equal_var
=
True
)
t_p_value
=
t_test
[
1
]
if
t_p_value
>
0.05
:
print
(
"策略前后两组数据无显著性差异,即该指标没有显著提升,p_value:
%
f"
%
t_p_value
)
else
:
print
(
"策略前后两组数据有显著性差异,即该指标获得显著提升,p_value:
%
f"
%
t_p_value
)
else
:
#认为数据方差不具有齐性,equal_var=false
t_test
=
ttest_ind
(
x
,
y
,
equal_var
=
False
)
t_p_value
=
t_test
[
1
]
if
t_p_value
>
0.05
:
print
(
"策略前后两组数据无显著性差异,即该指标没有显著提升,p_value:
%
f"
%
t_p_value
)
else
:
print
(
"策略前后两组数据有显著性差异,即该指标获得显著提升,p_value:
%
f"
%
t_p_value
)
###假设检验,判断是否具有显著性
#新用户cvr假设检验
crv_new_ttest
=
t_test
(
x_crv_new
,
y_crv_new
)
#老用户cvr假设检验
crv_old_ttest
=
t_test
(
x_crv_old
,
y_crv_old
)
#新用户ct_cvr假设检验
ctcrv_new_ttest
=
t_test
(
x_ctcrv_new
,
y_ctcrv_new
)
#老用户ct_cvr假设检验
ctcrv_old_ttest
=
t_test
(
x_ctcrv_old
,
y_ctcrv_old
)
#新用户ctr假设检验
ctr_new_ttest
=
t_test
(
x_ctr_new
,
y_ctr_new
)
#老用户ctr假设检验
ctr_old_ttest
=
t_test
(
x_ctr_old
,
y_ctr_old
)
##新用户ctr(on_click_diary_card)假设检验
ctr_new_o_ttest
=
t_test
(
x_ctr_new_o
,
y_ctr_new_o
)
#老用户ctr(on_click_diary_card)假设检验
ctr_old_o_ttest
=
t_test
(
x_ctr_old_o
,
y_ctr_old_o
)
###############推荐策略不变的情况下数据假设检验##############
#1 计算每日指标卡方检验
#自动获取5日前的日期
def
get_fivedate
():
#自动获取10日前的日期,如"2018-07-28"
"""
:rtype : str
"""
today
=
datetime
.
date
.
today
()
someday
=
today
-
datetime
.
timedelta
(
days
=
5
)
someday
=
someday
.
strftime
(
"
%
Y-
%
m-
%
d"
)
return
someday
five_days
=
get_fivedate
()
#获取最近5天的数据
def
chi_DATA_recently
(
x
,
y
,
z
):
sql_cid
=
"select AVG({0}),AVG({1}) from {2}
\
where stat_date >= five_days and stat_date<yesterday union all select {0},{1} from {2} where stat_date=yesterday}"
.
format
(
x
,
y
,
z
)
CVR_DATA_recently
=
con_sql
(
sql_cid
)[
0
][
0
]
return
CVR_DATA_recently
chi_cvr_new
=
chi_cvr_old
=
chi_ctcvr_new
=
chi_ctcvr_old
=
def
chi_cal
(
data
):
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